Present Monday: Austin, Gordon, Brieanna, Jaimie

Present Tuesday: Gordon, Brieanna, Jaimie

• Ask Sharif to meet with us

• 3D plot: 2011, 2012 normalized and unnormalized for comparison
• LA more variation, but smoother
• Moving Average and Decimation/Downsample
• D and X from 2011:
• X = 8:00-4:00 (input data)
• D = 9:00-5:00 (normalized and downsampled, column)
• Y = Least Squares Equation = W.T(X) row = X.T(W) column
• Unnormalized = D bar, Y bar
  • Statistical:
    • Y - sig*Y + m
    • D - sig*D + m
  • Zenith:
    • Y*cos(theta)
    • D*cos(theta)
• W = (XX.T)^(-1)*XD ⇐ XX.T*W = XD
• E^2 Averaged = (D-Y)^2 for every time
• MSE = 1/m (||D-Y||)^2
• Don't need r bc cancel
• Ask Seyed what he did for decimation/downsample
  • 1) Average and down sample
  • 2) Take moving average
  • Overlapping data points:
    • 8:01-8:10 moving average
    • Then downsample
• Filter then normalize
• Normalize then filter
  • Normalization: mean then deviation
  • Zenith Angle Filter: Zenith angle is from the 5th minute, 5 irradiance is averaged
    • Bias term
      • Number of taps is # present + number before
        • 1 tap = present
        • 2 tap = present + future (5 mins in our case)
      • In weight matrix
        • Row of 1's to the matrix to get bias term
  • Statistical normalization: -mean/deviation
    • Standard normalization 0 mean
• Estimate will need to be given back after the normalization
• 3D plot for both zenith and stat normalization
• Plot MSE
  • y = MSE, x = # taps
  • Train error goes down, test error decreases until flattens or increases
• Check 2012 MSE test against 2011 MSE train
  • Keep 2011 W
  • Get new X, D to compute new Y
• Flip: 2011 MSE test against 2012 MSE train
• Correlation is 3D plot
  • R=(1/m)XX.T= 10×10 matrix that is symmetric

Contributing authors:

Created by jobatake on 2016/10/19 02:43.